This project implements a geometric transformer that learns geometric features for robust 3D point cloud registration. It encodes pairwise distances and triplet-wise angles to achieve transformation invariance and low-overlap robustness. The method achieved oral presentation at CVPR 2022 and demonstrated significant improvements in inlier ratio and registration recall on challenging benchmarks like 3DLoMatch and KITTI.